class_docstr: Extracts lead sheet fragments from a quantized NoteSequence.
class_docstr_tok: ["Extracts","lead","sheet","fragments","from","a","quantized","NoteSequence","."]
code: def __init__(
      self,
      min_bars=7,
      max_steps=512,
      min_unique_pitches=5,
      gap_bars=1.0,
      ignore_polyphonic_notes=False,
      filter_drums=True,
      require_chords=True,
      all_transpositions=True,
      name=None,
  ):
      super(LeadSheetExtractor, self).__init__(
          input_type=music_pb2.NoteSequence,
          output_type=lead_sheets_lib.LeadSheet,
          name=name,
      )
      self._min_bars = min_bars
      self._max_steps = max_steps
      self._min_unique_pitches = min_unique_pitches
      self._gap_bars = gap_bars
      self._ignore_polyphonic_notes = ignore_polyphonic_notes
      self._filter_drums = filter_drums
      self._require_chords = require_chords
      self._all_transpositions = all_transpositions
  
file_path: magenta/magenta/pipelines/lead_sheet_pipelines.py
imports: import copy
  from magenta.pipelines import chord_pipelines
  from magenta.pipelines import melody_pipelines
  from magenta.pipelines import pipeline
  from magenta.pipelines import statistics
  from note_seq import chord_symbols_lib
  from note_seq import chords_lib
  from note_seq import events_lib
  from note_seq import lead_sheets_lib
  from note_seq import LeadSheet
  from note_seq import sequences_lib
  from note_seq.protobuf import music_pb2
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new LeadSheetExtractor object from inherited base classes: pipeline with example: obj = LeadSheetExtractor(min_bars, max_steps, min_unique_pitches, gap_bars, ignore_polyphonic_notes, filter_drums, require_chords, all_transpositions, name)